Medical Oncology

, 35:147 | Cite as

DNA methylation marker to estimate the breast cancer cell fraction in DNA samples

  • Hiroki Ishihara
  • Satoshi Yamashita
  • Satoshi Fujii
  • Kazunari Tanabe
  • Hirofumi Mukai
  • Toshikazu UshijimaEmail author
Original Paper


Estimation of the cancer cell fraction in breast cancer tissue is important for exclusion of samples unsuitable for multigene prognostic assays and a variety of molecular analyses for research. Here, we aimed to establish a breast cancer cell fraction marker based on DNA methylation. First, we screened genes unmethylated in non-cancerous mammary tissues and methylated in breast cancer tissues using microarray data from the TCGA database, and isolated 12 genes. Among them, four genes were selected as candidate marker genes without a high incidence of copy number alterations and with broad coverage across patients. Bisulfite pyrosequencing analysis of additional breast cancer biopsy specimens purified by laser capture microdissection (LCM) excluded two genes, and a combination of SIM1 and CCDC181 was finally selected as a fraction marker. In further additional specimens without LCM purification, the fraction marker was substantially methylated (≥ 20%) with high incidence (50/51). The cancer cell fraction estimated by the fraction marker was significantly correlated with that estimated by microscopic examination (p < 0.0001). Performance of a previously established marker, HSD17B4 methylation, which predicts therapeutic response of HER2-positive breast cancer to trastuzumab, was improved after the correction of cancer cell fraction by the fraction marker. In conclusion, we successfully established a breast cancer cell fraction marker based on DNA methylation.


DNA methylation Cancer cell fraction Breast cancer Trastuzumab HER2 Cancer cell content HSD17B4 



The authors are grateful to Drs. K. Ichimura, Y. Matsushita, and M. Kitahara of Division of Brain Tumor Translational Research in the National Cancer Center Research Institute for their technical assistance with the usage of the PSQ 96 Pyrosequencing System.


This research was supported by the Program for Promoting Platform of Genomics based Drug Discovery (Grant Number 18kk0305004h0003) from the Japan Agency for Medical Research and Development, AMED.

Compliance with ethical standards

Conflict of interest

The authors state no conflicts of interest regarding this work.

Ethical approval

Written informed consent was obtained from all participants.

Supplementary material

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Supplementary material 1 (DOCX 14 KB)
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Supplementary material 3 (TIFF 760 KB)
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Supplementary material 4 (XLSX 28 KB)


  1. 1.
    Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet. 2010;11(10):685–96. Scholar
  2. 2.
    Gusnanto A, Wood HM, Pawitan Y, Rabbitts P, Berri S. Correcting for cancer genome size and tumour cell content enables better estimation of copy number alterations from next-generation sequence data. Bioinformatics. 2012;28(1):40–7. Scholar
  3. 3.
    Roma C, Esposito C, Rachiglio AM, Pasquale R, Iannaccone A, Chicchinelli N, et al. Detection of EGFR mutations by TaqMan mutation detection assays powered by competitive allele-specific TaqMan PCR technology. BioMed Res Int. 2013;2013:385087. Scholar
  4. 4.
    Yau C, Mouradov D, Jorissen RN, Colella S, Mirza G, Steers G, et al. A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data. Genome Biol. 2010;11(9):R92. Scholar
  5. 5.
    Takahashi T, Matsuda Y, Yamashita S, Hattori N, Kushima R, Lee YC, et al. Estimation of the fraction of cancer cells in a tumor DNA sample using DNA methylation. PloS ONE. 2013;8(12):e82302. Scholar
  6. 6.
    Zong L, Hattori N, Yoda Y, Yamashita S, Takeshima H, Takahashi T, et al. Establishment of a DNA methylation marker to evaluate cancer cell fraction in gastric cancer. Gastric Cancer. 2016;19(2):361–9. Scholar
  7. 7.
    Heller G, Babinsky VN, Ziegler B, Weinzierl M, Noll C, Altenberger C, et al. Genome-wide CpG island methylation analyses in non-small cell lung cancer patients. Carcinogenesis. 2013;34(3):513–21. Scholar
  8. 8.
    Shen J, Wang S, Zhang YJ, Wu HC, Kibriya MG, Jasmine F, et al. Exploring genome-wide DNA methylation profiles altered in hepatocellular carcinoma using Infinium HumanMethylation 450 BeadChips. Epigenetics. 2013;8(1):34–43. Scholar
  9. 9.
    Wu Y, Davison J, Qu X, Morrissey C, Storer B, Brown L, et al. Methylation profiling identified novel differentially methylated markers including OPCML and FLRT2 in prostate cancer. Epigenetics. 2016;11(4):247–58. Scholar
  10. 10.
    Harada T, Yamamoto E, Yamano HO, Nojima M, Maruyama R, Kumegawa K, et al. Analysis of DNA methylation in bowel lavage fluid for detection of colorectal cancer. Cancer Prev Res (Philadelphia). 2014;7(10):1002–10. Scholar
  11. 11.
    Okamoto Y, Sawaki A, Ito S, Nishida T, Takahashi T, Toyota M, et al. Aberrant DNA methylation associated with aggressiveness of gastrointestinal stromal tumour. Gut. 2012;61(3):392–401. Scholar
  12. 12.
    Yan PS, Venkataramu C, Ibrahim A, Liu JC, Shen RZ, Diaz NM, et al. Mapping geographic zones of cancer risk with epigenetic biomarkers in normal breast tissue. Clin Cancer Res. 2006;12(22):6626–36. Scholar
  13. 13.
    Stirzaker C, Zotenko E, Song JZ, Qu W, Nair SS, Locke WJ, et al. Methylome sequencing in triple-negative breast cancer reveals distinct methylation clusters with prognostic value. Nat Commun. 2015;6:5899. Scholar
  14. 14.
    Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817–26. Scholar
  15. 15.
    Mook S, Schmidt MK, Viale G, Pruneri G, Eekhout I, Floore A, et al. The 70-gene prognosis-signature predicts disease outcome in breast cancer patients with 1–3 positive lymph nodes in an independent validation study. Breast Cancer Res Treat. 2009;116(2):295–302. Scholar
  16. 16.
    Fujii S, Yamashita S, Yamaguchi T, Takahashi M, Hozumi Y, Ushijima T, et al. Pathological complete response of HER2-positive breast cancer to trastuzumab and chemotherapy can be predicted by HSD17B4 methylation. Oncotarget. 2017;8(12):19039–48. Scholar
  17. 17.
    Shigematsu Y, Niwa T, Yamashita S, Taniguchi H, Kushima R, Katai H, et al. Identification of a DNA methylation marker that detects the presence of lymph node metastases of gastric cancers. Oncol Lett. 2012;4(2):268–74. Scholar
  18. 18.
    Yamashita S, Takahashi S, McDonell N, Watanabe N, Niwa T, Hosoya K, et al. Methylation silencing of transforming growth factor-beta receptor type II in rat prostate cancers. Cancer Res. 2008;68(7):2112–21. Scholar
  19. 19.
    Yoshida T, Yamashita S, Takamura-Enya T, Niwa T, Ando T, Enomoto S, et al. Alu and Satalpha hypomethylation in Helicobacter pylori-infected gastric mucosae. Int J Cancer. 2011;128(1):33–9. Scholar
  20. 20.
    Takahashi T, Yamahsita S, Matsuda Y, Kishino T, Nakajima T, Kushima R, et al. ZNF695 methylation predicts a response of esophageal squamous cell carcinoma to definitive chemoradiotherapy. J Cancer Res Clin Oncol. 2015;141(3):453–63. Scholar
  21. 21.
    Gyobu K, Yamashita S, Matsuda Y, Igaki H, Niwa T, Oka D, et al. Identification and validation of DNA methylation markers to predict lymph node metastasis of esophageal squamous cell carcinomas. Ann Surg Oncol. 2011;18(4):1185–94. Scholar
  22. 22.
    Robinson MD, Stirzaker C, Statham AL, Coolen MW, Song JZ, Nair SS, et al. Evaluation of affinity-based genome-wide DNA methylation data: effects of CpG density, amplification bias, and copy number variation. Genome Res. 2010;20(12):1719–29. Scholar
  23. 23.
    Jung SH, Lee A, Yim SH, Hu HJ, Choe C, Chung YJ. Simultaneous copy number gains of NUPR1 and ERBB2 predicting poor prognosis in early-stage breast cancer. BMC Cancer. 2012;12:382. Scholar
  24. 24.
    Song S, Nones K, Miller D, Harliwong I, Kassahn KS, Pinese M, et al. qpure: a tool to estimate tumor cellularity from genome-wide single-nucleotide polymorphism profiles. PloS ONE. 2012;7(9):e45835. Scholar
  25. 25.
    Su X, Zhang L, Zhang J, Meric-Bernstam F, Weinstein JN. PurityEst: estimating purity of human tumor samples using next-generation sequencing data. Bioinformatics. 2012;28(17):2265–6. Scholar
  26. 26.
    Curtius K, Wright NA, Graham TA. An evolutionary perspective on field cancerization. Nat Rev Cancer. 2018;18(1):19–32. Scholar
  27. 27.
    Cheng AS, Culhane AC, Chan MW, Venkataramu CR, Ehrich M, Nasir A, et al. Epithelial progeny of estrogen-exposed breast progenitor cells display a cancer-like methylome. Cancer Res. 2008;68(6):1786–96. Scholar
  28. 28.
    Baba Y, Ishimoto T, Kurashige J, Iwatsuki M, Sakamoto Y, Yoshida N, et al. Epigenetic field cancerization in gastrointestinal cancers. Cancer Lett. 2016;375(2):360–6. Scholar
  29. 29.
    Asada K, Nakajima T, Shimazu T, Yamamichi N, Maekita T, Yokoi C, et al. Demonstration of the usefulness of epigenetic cancer risk prediction by a multicentre prospective cohort study. Gut. 2015;64(3):388–96. Scholar
  30. 30.
    Asada K, Ando T, Niwa T, Nanjo S, Watanabe N, Okochi-Takada E, et al. FHL1 on chromosome X is a single-hit gastrointestinal tumor-suppressor gene and contributes to the formation of an epigenetic field defect. Oncogene. 2013;32(17):2140–9. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Division of EpigenomicsNational Cancer Center Research InstituteTokyoJapan
  2. 2.Department of UrologyTokyo Women’s Medical UniversityTokyoJapan
  3. 3.Division of Pathology, Exploratory Oncology Research and Clinical Trial CenterNational Cancer CenterKashiwaJapan
  4. 4.Department of Breast and Medical OncologyNational Cancer Center Hospital EastKashiwaJapan

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